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FUSIONOFDAUBECHIESWAVELETCOEFFICIENTSFORHUMANFACERECOGNITIONMRINALKANTIBHOWMIK1,DEBOTOSHBHATTACHARJEE2,MITANASIPURI2,DIPAKKUMARBASU2,ANDMAHANTAPASKUNDU21DEPARTMENTOFCOMPUTERSCIENCEANDENGINEERING,TRIPURAUNIVERSITYSURYAMANINAGAR799130,TRIPURA,INDIAEMAILMKB_CSEYAHOOCOIN2DEPARTMENTOFCOMPUTERSCIENCEANDENGINEERING,JADAVPURUNIVERSITYKOLKATA700032,INDIAAICTEEMERITUSFELLOWEMAILDEBOTOSHINDIATIMESCOM,MITANASIPURI,DIPAKKBASUGMAILCOM,MKUNDUCSEJDVUACINABSTRACTINTHISPAPERFUSIONOFVISUALANDTHERMALIMAGESINWAVELETTRANSFORMEDDOMAINHASBEENPRESENTEDHERE,DAUBECHIESWAVELETTRANSFORM,CALLEDASD2,COEFFICIENTSFROMVISUALANDCORRESPONDINGCOEFFICIENTSCOMPUTEDINTHESAMEMANNERFROMTHERMALIMAGESARECOMBINEDTOGETFUSEDCOEFFICIENTSAFTERDECOMPOSITIONUPTOFIFTHLEVELLEVEL5FUSIONOFCOEFFICIENTSISDONEINVERSEDAUBECHIESWAVELETTRANSFORMOFTHOSECOEFFICIENTSGIVESUSFUSEDFACEIMAGESTHEMAINADVANTAGEOFUSINGWAVELETTRANSFORMISTHATITISWELLSUITEDTOMANAGEDIFFERENTIMAGERESOLUTIONANDALLOWSTHEIMAGEDECOMPOSITIONINDIFFERENTKINDSOFCOEFFICIENTS,WHILEPRESERVINGTHEIMAGEINFORMATIONFUSEDIMAGESTHUSFOUNDAREPASSEDTHROUGHPRINCIPALCOMPONENTANALYSISPCAFORREDUCTIONOFDIMENSIONSANDTHENTHOSEREDUCEDFUSEDIMAGESARECLASSIFIEDUSINGAMULTILAYERPERCEPTRONFOREXPERIMENTSIRISTHERMAL/VISUALFACEDATABASEWASUSEDEXPERIMENTALRESULTSSHOWTHATTHEPERFORMANCEOFTHEAPPROACHPRESENTEDHEREACHIEVESMAXIMUMSUCCESSRATEOF100INMANYCASESINDEXTERMSTHERMALIMAGE,DAUBECHIESWAVELETTRANSFORM,FUSION,PRINCIPALCOMPONENTANALYSISPCA,MULTILAYERPERCEPTRON,CLASSIFICATIONIINTRODUCTIONMANYMETHODSHAVEBEENPROPOSEDFORFACERECOGNITIONFUSIONOFIMAGESEXPLOITSSYNERGISTICINTEGRATIONOFIMAGESOBTAINEDFROMMULTIPLESENSORSANDBYTHATITCANGATHERDATAINDIFFERENTFORMSLIKEAPPEARANCEANDANATOMICALINFORMATIONOFTHEFACE,WHICHENRICHESTHESYSTEMINIMPROVINGRECOGNITIONACCURACY9ASAMATTEROFFACTFUSIONOFIMAGESHASALREADYESTABLISHEDITSIMPORTANCEINCASEOFIMAGEANALYSIS,RECOGNITION,ANDCLASSIFICATIONFORINSTANCE,AGLIKAGYAOUROVAETAL10TRIEDTOIMPLEMENTEDPIXELBASEDFUSIONSCHEMEINTHEWAVELETDOMAIN,ANDFEATUREBASEDFUSIONINTHEEIGENSPACEDOMAINALTHOUGHTHEIRFUSIONAPPROACHWASNOTABLETOFULLYDISCOUNTILLUMINATIONEFFECTSPRESENTINTHEVISIBLEIMAGESBUTTHEYSHOWEDSUBSTANTIALIMPROVEMENTSINOVERALLRECOGNITIONPERFORMANCETHEYALSOINDICATEDTHATIRBASEDRECOGNITIONPERFORMANCEDEGRADESSERIOUSLYWHENEYEGLASSESAREPRESENTINTHEPROBEIMAGEBUTNOTINTHEGALLERYIMAGEANDVICEVERSAONTHEOTHERHANDFORTHEIMPROVEMENTOFTHEPERFORMANCEOFFACERECOGNITIONWHENFACEIMAGESAREOCCLUDEDBYWEARINGEYEGLASSES,JEONGSEONPARKETAL11FIRSTDETECTTHEREGIONSOCCLUDEDBYTHEGLASSESANDGENERATEANATURALLOOKINGFACIALIMAGEWITHOUTGLASSESBYRECURSIVEERRORCOMPENSATIONUSINGPCARECONSTRUCTIONTHEYPROPOSEDANEWGLASSESREMOVALMETHODBASEDONRECURSIVEERRORCOMPENSATIONUSINGPCARECONSTRUCTIONGEORGEBEBISETAL12INVESTIGATEDTHATTWODIFFERENTFUSIONSCHEMESLIKEFIRSTONEISPIXELBASEDANDOPERATESINTHEWAVELETDOMAINUSINGHAARTRANSFORMS,WHILETHESECONDONEISFEATUREBASEDANDOPERATESINTHEEIGENSPACEDOMAININBOTHCASES,THEYEMPLOYASIMPLEANDGENERALFRAMEWORKBASEDONGENETICALGORITHMSGASTOFINDANOPTIMUMFUSIONSTRATEGYAMITARANETAL13DEMONSTRATEDTHESPECTRALBANDINVARIANTWAVEMACHFILTERSWHICHAREDESIGNEDUSINGIMAGESOFCCD/IRCAMERAFUSEDBYDAUBECHIESWAVELETTRANSFORMANDIMPLEMENTEDINHYBRIDDIGITALOPTICALCORRELATORARCHITECTURETOIDENTIFYMULTIPLETARGETSINASCENETHEYHAVEFUSIONOFINFRAREDANDCCDCAMERABECAUSETHEPERFORMANCEOFCCDCAMERAISBETTERUNDERGOODILLUMINATIONCONDITIONSWHEREASIRCAMERAGIVESABETTEROUTPUTUNDERPOORILLUMINATIONORINTHENIGHTCONDITIONSALSOTHEAUTHORSIN14PROPOSEDDATAFUSIONOFVISUALANDTHERMALIMAGESUSINGGABORFILTERINGTECHNIQUEWHICHEXTRACTSFACIALFEATURES,AREUSEDASAFACERECOGNITIONTECHNIQUEITHASBEENFOUNDTHATBYUSINGTHEPROPOSEDFUSIONTECHNIQUEGABORFILTERCANRECOGNIZEFACEEVENWITHVARIABLEEXPRESSIONSANDLIGHTINTENSITIES,BUTNOTINEXTREMECONDITIONDIEGOASOCOLINSKYANDANDREASELINGER15CONSIDEREDOUTDOORANDINDOORIMAGINGCONDITIONSFORTHERMALIMAGING,ANDONEOFFEWTODOSOEVENFORVISIBLEFACERECOGNITIONITISCLEARFROMTHEIREXPERIMENTSTHATFACERECOGNITIONOUTDOORSWITHVISIBLEIMAGERYISFARLESSACCURATETHANWHENPERFORMEDUNDERFAIRLYCONTROLLEDINDOORCONDITIONSFOROUTDOORUSE,THERMALIMAGINGPROVIDESUSWITHACONSIDERABLEPERFORMANCEBOOSTTHERMALRECOGNITIONPERFORMANCESUFFERSAMODERATEDECAYWHENPERFORMEDOUTSIDEAGAINSTANINDOORENROLLMENTSET,PROBABLYASARESULTOFENVIRONMENTALCHANGESJINGUHEOETAL16DESCRIBESCOMPARISONRESULTSONTHREEFUSIONBASEDFACERECOGNITIONTECHNIQUESLIKEDATAFUSIONOFVISUALANDTHERMALIMAGESDF,DECISIONFUSIONWITHHIGHESTMATCHINGSCOREFH,ANDDECISIONFUSIONWITHAVERAGEMATCHINGSCOREFAANDSHOWEDTHATFUSIONBASEDFACERECOGNITIONTECHNIQUESOUTPERFORMEDINDIVIDUALVISUALANDTHERMALFACERECOGNIZERSUNDERILLUMINATIONVARIATIONSANDFACIALEXPRESSIONSFROMTHEMDECISIONFUSIONWITHAVERAGEMATCHINGSCORECONSISTENTLYDEMONSTRATEDSUPERIORRECOGNITIONACCURACIESASPERTHEIRRESULTSIOANNISPAVLIDISANDPETERSYMOSEK17DEMONSTRATEDATHEORETICALANDEXPERIMENTALARGUMENTTHATADUALBANDUPPERANDLOWERBANDFUSIONSYSTEMINTHENEARINFRAREDCANSEGMENTHUMANFACESMUCHMOREACCURATELYTHANTRADITIONALVISIBLEBANDDISGUISEFACEDETECTIONSYSTEMSDIEGOASOCOLINSKYANDANDREASELINGER18PERFORMEDACLEARANALYSISTHATLWIRIMAGERYOFHUMANFACESISNOTONLYAVALIDBIOMETRIC,BUTALMOSTSURELYASUPERIORONETOCOMPARABLEVISIBLEIMAGERYXINCHEN,PATRICKJFLYNNANDKEVINWBOWYER19SHOWEDTHATTHECOMBINATIONOFIRPLUSVISIBLECANOUTPERFORMEITHERIRORVISIBLEALONETHEYFINDACOMBINATIONMETHODTHATCONSIDERSTHEDISTANCEVALUESPERFORMSBETTERTHANONETHATONLYCONSIDERSRANKSCHRISTOPHERKEVELANDETAL20INTRODUCEDAMETHODOLOGYFORTRACKINGHUMANFACESINCALIBRATEDTHERMALINFRAREDIMAGERYOFLWIRANDMWIRINDOORIMAGESEQUENCESHKEKENELANDBSANKUR7PROPOSEDMULTIRESOLUTIONANALYSISONSUBSPACEANALYSISDOMAINLIKEPCAANDICAINTHISWORK,ATECHNIQUEFORHUMANFACERECOGNITIONBASEDONFUSIONINWAVELETTRANSFORMEDDOMAINISPROPOSEDANDDISCUSSEDSUBSEQUENTLYIISYSTEMOVERVIEWTHEBLOCKDIAGRAMOFTHESYSTEMISGIVENINFIG1ALLTHEPROCESSINGSTEPSUSEDINTHISPAPERARESHOWNINTHEBLOCKDIAGRAMINTHEFIRSTSTEP,DECOMPOSITIONOFBOTHTHETHERMALANDVISUALIMAGESUPTOLEVELFIVEHASBEENDONEUSINGWAVELETTHENFUSEDIMAGEISGENERATEDFROMBOTHTHEDECOMPOSEDIMAGESTHESETRANSFORMEDIMAGESSEPARATEDINTOTWOGROUPSNAMELYTRAININGSETANDTESTINGSETTHEEIGENSPACEISNAMEDASFUSEDEIGENSPACESONCETHISPROJECTIONISDONE,THENEXTSTEPISTOUSEACLASSIFIERTOCLASSIFYTHEMAMULTILAYERPERCEPTRONHASBEENUSEDFORTHISPURPOSEAIMAGEDECOMPOSITIONWAVELETTRANSFORMSAREMULTIRESOLUTIONIMAGEDECOMPOSITIONTOOLTHATPROVIDEAVARIETYOFCHANNELSREPRESENTINGTHEIMAGEFEATUREBYDIFFERENTFREQUENCYSUBBANDSATMULTISCALEITISAFAMOUSTECHNIQUEINANALYZINGSIGNALSWHENDECOMPOSITIONISPERFORMED,THEAPPROXIMATIONANDDETAILCOMPONENTCANBESEPARATED1THEDAUBECHIESWAVELETDB2DECOMPOSEDUPTOFIVELEVELSHASBEENUSEDHEREFORIMAGEFUSIONTHESEWAVELETSAREUSEDHEREBECAUSETHEYAREREALANDCONTINUOUSINNATUREANDHAVELEASTROOTMEANSQUARERMSERRORCOMPAREDTOOTHERWAVELETS56FUSEDWAVELETCOEFFICIENTMAPSCLASSESRECOGNITIONRESULTCLASSIFICATIONMULTILAYERPERCEPTRONNEURALNETWORKDIMENSIONREDUCTIONDECISIONFUSIONBYAVERAGINGWAVELETCOEFFICIENTUPTOLEVEL5WAVELETCOEFFICIENTUPTOLEVEL5VISUALIMAGETHERMALIMAGEIDWTDWTDWTPCARECONSTRUCTEDFUSEDIMAGEFIGURE1BLOCKDIAGRAMOFTHESYSTEMPRESENTEDHEREDAUBECHIESWAVELETSAREAFAMILYOFORTHOGONALWAVELETSDEFININGADISCRETEWAVELETTRANSFORMANDCHARACTERIZEDBYAMAXIMALNUMBEROFVANISHINGMOMENTSFORSOMEGIVENSUPPORTTHISKINDOF2DDWTAIMSTODECOMPOSETHEIMAGEINTOAPPROXIMATIONCOEFFICIENTSCAANDDETAILEDCOEFFICIENTCH,CVANDCDHORIZONTAL,VERTICALANDDIAGONALOBTAINEDBYWAVELETDECOMPOSITIONOFTHEINPUTIMAGEXTHEFIRSTPARTOFFIG1SHOWINGAFTERDECOMPOSITIONOFTWOIMAGESCA,CH,CV,CDDWT2X,WNAME1CA,CH,CV,CDDWT2X,LO_D,HI_D2EQUATION1,WNAMEISTHENAMEOFTHEWAVELETUSEDFORDECOMPOSITIONEQUATION2LO_DDECOMPOSITIONLOWPASSFILTERANDHI_DDECOMPOSITIONHIGHPASSFILTERWAVELETDECOMPOSITIONFILTERSTHISKINDOFTWODIMENSIONALDWTLEADSTOADECOMPOSITIONOFAPPROXIMATIONCOEFFICIENTSATLEVELJINFOURCOMPONENTSTHEAPPROXIMATIONATLEVELJ1,ANDTHEDETAILSINTHREEORIENTATIONSHORIZONTAL,VERTICAL,ANDDIAGONALTHEFIG2DESCRIBESTHEALGORITHMICBASICDECOMPOSITIONSTEPSFORIMAGEWHERE,ABLOCKWITHADOWNARROWINDICATESDOWNSAMPLINGOFCOLUMNSANDROWSANDCA,CH,CVANDCDARETHECOEFFICIENTVECTORS234BIMAGERECONSTRUCTIONTHEMORETHEDECOMPOSITIONSCHEMEISBEINGREPEATED,THEMORETHEAPPROXIMATIONIMAGECONCENTRATESINTHELOWFREQUENCYENERGYTOGETRIDOFTHEILLUMINATIONEFFECTSTHATMAYINFLUENCETHERECOGNITIONRATE,THECOEFFICIENTSINWAVELETAPPROXIMATIONSUBBANDISSETTOZEROCONSEQUENTLYTHERECONSTRUCTIONPROCESSISPERFORMEDUSINGINVERSEOFDWTIDWTFINALLYTHERECONSTRUCTTEDIMAGEISUSEDASTHEINPUTTOPCAFORRECOGNITIONXIDWT2CA,CH,CV,CD,WNAME3XIDWT2CA,CH,CV,CD,LO_R,HI_R4IDWTUSESTHEWAVELETWNAMETOCOMPUTETHESINGLELEVELRECONSTRUCTIONOFANIMAGEX,BASEDONAPPROXIMATIONMATRIXCAANDDETAILEDMATRICESCH,CVANDCDHORIZONTAL,VERTICALANDDIAGONALRESPECTIVELYBYTHEEQUATIONNO4,WECANRECONSTRUCTTHEIMAGEUSINGFILTERSLO_RRECONSTRUCTLOWPASSANDHI_RRECONSTRUCTHIGHPASSINTHEFIG3WEHAVESHOWNTHEALGORITHMICBASICRECONSTRUCTIONSTEPSFORANIMAGECPRINCIPALCOMPONENTANALYSISPRINCIPALCOMPONENTANALYSISPCAISBASEDONTHESECONDORDERSTATISTICSOFTHEINPUTIMAGE,WHICHTRIESTOATTAINANOPTIMALREPRESENTATIONTHATMINIMIZESTHERECONSTRUCTIONERRORINALEASTSQUARESSENSEEIGENVECTORSOFTHECOVARIANCEMATRIXOFTHEFACEIMAGESCONSTITUTETHEEIGENFACESTHEDIMENSIONALITYOFTHEFACEFEATURESPACEISREDUCEDBYSELECTINGONLYTHEEIGENVECTORSPOSSESSINGSIGNIFICANTLYLARGEEIGENVALUESONCETHENEWFACESPACEISCONSTRUCTED,WHENATESTIMAGEARRIVES,ITISPROJECTEDONTOTHISFACESPACETOYIELDTHEFEATUREVECTORTHEREPRESENTATIONCOEFFICIENTSINTHECONSTRUCTEDFACESPACETHECLASSIFIERDECIDESFORTHEIDENTITYOFTHEINDIVIDUAL,ACCORDINGTOASIMILARITYSCOREBETWEENTHETESTIMAGESFEATUREVECTORANDTHEPCAFEATUREVECTORSOFTHEINDIVIDUALSINTHEDATABASE723DANNUSINGBACKPROPAGATIONWITHMOMENTUMNEURALNETWORKS,WITHTHEIRREMARKABLEABILITYTODERIVEMEANINGFROMCOMPLICATEDORIMPRECISEDATA,CANBEUSEDTOEXTRACTPATTERNSANDDETECTTRENDSTHATARETOOCOMPLEXTOBENOTICEDBYEITHERHUMANSOROTHERCOMPUTERTECHNIQUESATRAINEDNEURALNETWORKCANBETHOUGHTOFASAN“EXPERT”INTHECATEGORYOFINFORMATIONITHASBEENGIVENTOANALYZETHEBACKPROPAGATIONLEARNINGALGORITHMISONEOFTHEMOSTHISTORICALDEVELOPMENTSINNEURALNETWORKSITHASREAWAKENEDTHESCIENTIFICANDENGINEERINGCOMMUNITYTOTHEMODELINGANDPROCESSINGOFMANYQUANTITATIVEPHENOMENAUSINGNEURALNETWORKSTHISLEARNINGALGORITHMISAPPLIEDTOMULTILAYERFEEDFORWARDNETWORKSCONSISTINGOFPROCESSINGELEMENTSWITHCONTINUOUSDIFFERENTIABLEACTIVATIONFUNCTIONSSUCHNETWORKSASSOCIATEDWITHTHEBACKPROPAGATIONLEARNINGALGORITHMAREALSOCALLEDBACKPROPAGATIONNETWORKS82122232425IIIEXPERIMENTSRESULTSANDDISCUSSIONTHISWORKHASBEENSIMULATEDUSINGMATLAB7INAMACHINEOFTHECONFIGURATION213GHZINTELXEONQUADCOREPROCESSORAND1638400MBOFPHYSICALMEMORYWEANALYZETHEPERFORMANCEOFOURALGORITHMUSINGTHEIRISTHERMAL/VISUALFACEDATABASEAIRISTHERMAL/VISUALFACEDATABASEINTHISDATABASE,ALLTHETHERMALANDVISIBLEUNREGISTEREDFACEIMAGESARETAKENUNDERVARIABLEILLUMINATIONS,EXPRESSIONS,ANDPOSESTHEACTUALSIZEOFTHEIMAGESIS320X240PIXELSFORBOTHVISUALANDTHERMAL176250IMAGESPERPERSON,11IMAGESPERROTATIONPOSESFOREACHEXPRESSIONANDEACHILLUMINATIONTOTAL30CLASSESAREPRESENTINTHATDATABASEANDTHESIZEOFTHEDATABASEIS183GB28SOMEFUSEDIMAGESOFTHEIRCORRESPONDINGTHERMALANDVISUALIMAGESARESHOWNFIG4CACHCVCDLO_RHI_RLO_RHI_RCOLUMNSCOLUMNSCOLUMNSCOLUMNSLO_RWKEEPHI_R222222FIGURE3STEPSFORRECONSTRUCTIONOFANIMAGELO_DROWSHI_DROWSCAJ22LO_DHI_DLO_DHI_DCOLUMNSCOLUMNSCOLUMNSCOLUMNS2222CACHCVCDFIGURE2STEPSFORDECOMPOSITIONOFANIMAGEABCFIGURE4SAMPLEATHERMALIMAGESBVISUALIMAGESCCORRESPONDINGFUSEDIMAGESOFIRISDATABASEBTRAININGANDTESTINGATTHETIMEOFEXPERIMENT,WEUSEDTOTAL200VISUALAND200THERMALIMAGES,INWHICH20IMAGESPERCLASSOF10DIFFERENTCLASSESOFIRISDATABASEDAUBECHIESWAVELETTRANSFORMHASBEENUSEDTOGENERATEFUSEDIMAGESOFBOTHTHEDATABASESTHEDAUBECHIESWAVELETDB2DECOMPOSESTHEIMAGESUPTOFIVELEVELSTOMAKINGFUSIONIMAGEHERE,WECONSIDERHUMANFACERECOGNITIONUSINGMULTILAYERPERCEPTRONMLPTHEDAUBECHIESWAVELETDB2DECOMPOSESTHEIMAGESUPTOFIVELEVELSTOMAKINGFUSIONIMAGEHERE,WECONSIDERHUMANFACERECOGNITIONUSINGMULTILAYERPERCEPTRONMLPFORTHISRESEARCHPAPER,WEFIRSTTRAINOURNETWORKUSING100FUSEDIMAGESIE10IMAGESPERCLASSANDTHOSEARECONVERTEDFROMVISUALANDTHEIRCORRESPONDINGTHERMALIMAGESOFIRISTHERMAL/VISUALFACEDATABASEATTHETIMEOFTRAINING,MULTILAYERNEURALNETWORKWITHBACKPROPAGATIONHASBEENUSEDMOMENTUMALLOWSTHENETWORKTORESPONDNOTONLYTOTHELOCALGRADIENT,BUTALSOTORECENTTRENDSINTHEERRORSURFACEAFTERTRAININGTHENETWORK,ITWASTESTEDWITHATOTALOF10DIFFERENTRUNSFOR10DIFFERENTCLASSESANDALLTHEEXPERIMENTSRESULTSOFIRISDATABASEARESHOWNINTABLEIALLTHESEIMAGESCONTAINEDDIFFERENTKINDOFEXPRESSIONSAND70OFTHEIMAGESWERETAKENINDIFFERENTILLUMINATIONCONDITIONSTHECLASSESWITHDIFFERENTILLUMINATIONSWITHCHANGESINEXPRESSIONSARECLASS1,CLASS2,CLASS3,CLASS4,CLASS6,CLASS7ANDCLASS9,WHEREASCLASS5,CLASS8ANDCLASS10AREWITHCHANGESINEXPRESSIONSONLYINTHEFIGURE5,ALLTHERECOGNITIONRATESOFDIFFERENTCLASSESAREPRESENTEDFROMTHATFIGUREONECANOBSERVETHATTHECLASSES,CLASS3,CLASS6,CLASS7ANDCLASS10ARESHOWINGHIGHESTRECOGNITIONRATEOUTOFTHOSEFOURCLASSES,CLASS3ANDCLASS6CONTAINTHEIMAGESWITHCHANGESINILLUMINATIONASWELLASEXPRESSIONWHEREASOTHERTWOCLASSESCONTAINIMAGESWITHCHANGESISEXPRESSIONSONLYFIGURE5SHOWSRECOGNITIONRATEWITHFALSEREJECTIONTABLEIEXPERIMENTALRESULTSONIRISCLASSESUSEDNOOFTRAININGIMAGESNOOFTESTINGIMAGESWHICHARENOTUSEDDURINGTRAININGRECOGNITIONRATECLASS1101080CLASS2101070CLASS31010100CLASS4101070CLASS5101080CLASS61010100CLASS7101080CLASS81010100CLASS9101070CLASS101010100IVCONCLUSIONINTHISAFUSIONTECHNIQUEFORHUMANFACERECOGNITIONUSINGDAUBECHIESWAVELETTRANSFORMONTHEFACEIMAGESOFDIFFERENTILLUMINATIONWITHEXPRESSIONHASBEENPRESENTEDAFTERCOMPLETIONOFFUSION,IMAGESWEREPROJECTEDINTOANEIGENSPACETHOSEPROJECTEDFUSEDEIGENFACESARECLASSIFIEDUSINGAMULTILAYERPERCEPTRONEIGENSPACEISCONSTITUTEDBYTHEIMAGESBELONGTOTHETRAININGSETOFTHECLASSIFIER,WHICHISAMULTILAYERPERCEPTRONTHEEFFICIENCYOFTHESCHEMEHASBEENDEMONSTRATEDONIRISTHERMAL/VISUALFACEDATABASEWHICHCONTAINSIMAGESGATHEREDWITHVARYINGLIGHTING,FACIALEXPRESSION,POSEANDFACIALDETAILSTHESYSTEMHASACHIEVEDAMAXIMUMRECOGNITIONRATEOF100INFOURDIFFERENTCASESWITHANOVERALLRECOGNITIONRATEOF85ACKNOWLEDGMENTFIRSTAUTHORISTHANKFULTOTHEPROJECTENTITLED“DEVELOPMENTOFTECHNIQUESFORHUMANFACEBASEDONLINEAUTHENTICATIONSYSTEMPHASEI”SPONSOREDBYDEPARTMENTOFINFORMATIONTECHNOLOGYUNDERTHEMINISTRYOFCOMMUNICATIONSANDINFORMATIONTECHNOLOGY,NEWDELHI110003,GOVERNMENTOFINDIAVIDENO1214/08ESD,DATED27/0